Traffic sign classification remains a significant challenge for autonomous vehicles, particularly in recognizing targets of various scales and achieving real-time performance. The varying sizes of traffic signs can negatively affect detection accuracy. Additionally, changes in lighting when capturing input images can pose challenges for existing methods. Therefore, this research presents a new approach to enhance the performance of current traffic sign classification systems by combining the benefits of existing machine learning-based and deep learning-based methods through ensemble learning techniques. Experimental results show that the proposed method improved the performance of existing methods using the German Traffic Sign Recognition Benchmark dataset. Under normal input conditions, the proposed method achieved an accuracy of 95.77%, comparable to the state-of-the-art method YOLOv8, which had an accuracy of 94.97%. However, under difficult input conditions, the proposed method significantly improved YOLOv8's performance by approximately 7%.